First-Principle Microkinetic Modeling of Ethanol Dehydrogenation on Metal Catalyst Surfaces in Non-oxidative Environment: Design of Bimetallic Alloys

Khan, Tuhin Suvra; Jalid, Fatima; Haider, M. Ali

doi:10.1007/s11244-018-1028-9

Cited by 30 publications

(40 citation statements)

References 54 publications

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“…This was found in the case of PdZn nanoparticles [28], monometallic Cu [29,30], bi-or tri-metallic Cu alloys [29,[31][32][33], SiO 2 − supported Cu catalysts [34] and V/Mg − Al catalysts [35,36]. There is a considerable number of papers that use computational methods such as density functional theory or a combination of experimental methods and modelling tools to investigate correlations between catalyst activity and selectivity on the one hand and material characteristics such as crystal faces, monolayer coverages, etc., on the other [37][38][39][40][41]. Autthanit et al [42,43] investigated AgLi/TiO 2 and VO x /SBA-15 catalysts under non-oxidative and oxidative reaction conditions and found significantly higher conversions and acetaldehyde yields in the case of oxidative dehydrogenation.…”

Section: N2 Sorptionmentioning

confidence: 99%

“…Additionally, experiments and simulations have demonstrated that the combustion properties of pure iso-butanol and iso-butanol/gasoline blends, respectively, widely resemble the combustion characteristics of conventional gasoline. It has been demonstrated that using pure iso-butanol or iso-butanol/gasoline blends might even enhance the performance of internal combustion engines by reducing the quantities computational methods such as density functional theory or a combination of experimental methods and modelling tools to investigate correlations between catalyst activity and selectivity on the one hand and material characteristics such as crystal faces, monolayer coverages, etc., on the other [37][38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

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Ethanol Dehydrogenation: A Reaction Path Study by Means of Temporal Analysis of Products

et al. 2020

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Conventional fossil fuels such as gasoline or diesel should be substituted in the future by environmentally-friendly alternatives in order to reduce emissions in the transport sector and thus mitigate global warming. In this regard, iso-butanol is very promising as its chemical and physical properties are very similar to those of gasoline. Therefore, ongoing research deals with the development of catalytically-supported synthesis routes to iso-butanol, starting from renewably-generated methanol. This research has already revealed that the dehydrogenation of ethanol plays an important role in the reaction sequence from methanol to iso-butanol. To improve the fundamental understanding of the ethanol dehydrogenation step, the Temporal Analysis of Products (TAP) methodology was applied to illuminate that the catalysts used, Pt/C, Ir/C and Cu/C, are very active in ethanol adsorption. H2 and acetaldehyde are formed on the catalyst surfaces, with the latter quickly decomposing into CO and CH4 under the given reaction conditions. Based on the TAP results, this paper proposes a reaction scheme for ethanol dehydrogenation and acetaldehyde decomposition on the respective catalysts. The samples are characterized by means of N2 sorption and Scanning Transmission Electron Microscopy (STEM).

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Section: N2 Sorptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ethanol Dehydrogenation: A Reaction Path Study by Means of Temporal Analysis of Products

et al. 2020

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“…Owing to the industrial relevance of aliphatic alcohol dehydrogenation into aldehydes and ketones, it is of great significance to find and even design efficient catalysts for this reaction. Despite the extensive experimental and theoretical investigations reported to date on transition metals, including a first-principle predictive micro-kinetic study 49 , no combined experiment-theory report is available for fast, cost-effective, and time-efficient in silico screening of metals for alcohol dehydrogenation. Herein, we present a DFT-based micro-kinetic study to predict the activity of transition metals for alcohol dehydrogenation into ketones.…”

Section: Introductionmentioning

confidence: 99%

Identification of active catalysts for the acceptorless dehydrogenation of alcohols to carbonyls

et al. 2021

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Acceptorless dehydrogenation into carbonyls and molecular hydrogen is an attractive strategy to valorize (biobased) alcohols. Using 2-octanol dehydrogenation as benchmark reaction in a continuous reactor, a library of metal-supported catalysts is tested to validate the predictive level of catalytic activity for combined DFT and micro-kinetic modeling. Based on a series of transition metals, scaling relations are determined as a function of two descriptors, i.e. the surface binding energies of atomic carbon and oxygen. Then, a volcano-shape relation based on both descriptors is derived, paving the way to further optimization of active catalysts. Evaluation of 294 diluted alloys but also a series of carbides and nitrides with the volcano map identified 12 promising candidates with potentially improved activity for alcohol dehydrogenation, which provides useful guidance for experimental catalyst design. Further screening identifies β-Mo2N and γ-Mo2N exposing mostly (001) and (100) facets as potential candidates for alcohol dehydrogenation.

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“…[17] Furthermore, NiAg and PtAg are also calculating to show higher consumption rate (10 À 3 s À 1 ) of ethanol as compared to pure Ag catalyst, which is aligned with other alloy based studies where surfaces like Cu 3 Pt bimetallic alloys have shown similar improvement with production rate (10 À 4 s À 1 ) of acetaldehyde as compared to pure Cu (10 À 3 s À 1 ) catalyst. [66] Moreover, the ML model is predicting the same order of ethanol TOF on the SAAs of NiAu, PtAg, and NiAg, as estimated from DFT calculations, on the MKM plot (Figure 9), albeit with a significantly reduced calculation time and computational resource requirement. However, in the ML approach a caution must be emphasized since linear scaling relations are utilized to predict energies of the adsorbates and transition states of the elementary reaction steps in constructing the ab initio MKM.…”

Section: Discussionmentioning

confidence: 65%

Machine Learning Enabled Screening of Single Atom Alloys: Predicting Reactivity Trend for Ethanol Dehydrogenation

et al. 2021

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A machine learning (ML) approach implementing the gradient boosting regressor (GBR) algorithm is applied to predict the binding energies of oxygen (E O ) and carbon (E C ) atoms on single atom alloys (SAAs) of Cu, Ag and Au. Readily available periodic properties of the transition metals are utilized as input features in the model. Their relative contribution in adsorbate-metal interaction is assessed to develop a comprehensive descriptor. In test runs, the ML model is observed to predict E O and E C with significantly reduced errors (~0.2 eV). Further, ML approach is augmented with an ab initio microkinetic model (MKM) for non-oxidative dehydrogenation (NODH) of ethanol. The ML-MKM is calculated to yield higher turnover frequency for ethanol conversion on NiAu, NiAg, and PtAg SAAs as compared to their monometallic counterparts Au and Ag catalysts. Overall, the ML model provides a rationale for feature selection to synthesize catalytically active SAAs of group 11 elements using fast-track in silico screening.

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First-Principle Microkinetic Modeling of Ethanol Dehydrogenation on Metal Catalyst Surfaces in Non-oxidative Environment: Design of Bimetallic Alloys

Cited by 30 publications

References 54 publications

Ethanol Dehydrogenation: A Reaction Path Study by Means of Temporal Analysis of Products

Ethanol Dehydrogenation: A Reaction Path Study by Means of Temporal Analysis of Products

Identification of active catalysts for the acceptorless dehydrogenation of alcohols to carbonyls

Machine Learning Enabled Screening of Single Atom Alloys: Predicting Reactivity Trend for Ethanol Dehydrogenation

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